In today’s 3G world moving to 4G requires high data rate support in applications like multimedia services, internet access and video streaming services. Such applications are always in need of very high speed data rate support which increases the requirement of efficient usage of spectrum and high capacity systems. Thus the major challenges to be taken care of in designing the next generation wireless communications system should provide or accommodate capacity, the spectral efficiency, improved link reliability and multimedia services. So we can establish a distributed system in terms of multi-carrier, multi-antenna and coded pulse. It gives rise to hybrid technology based on DSSS, OFDM, and MIMO system which can be the ultimate solution for wireless cellular communication systems. In this paper we analysis the performance of MIMO-OFDM-DSSS system. This paper also includes comparison of performances of MIMO-OFDM-DSSS system with ZF and MMSE equalizer on the basis of BER using different modulation techniques in a scattering environment.

1. International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163
Volume 1 Issue 5 (June 2014) http://ijirae.com
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© 2014, IJIRAE- All Rights Reserved Page - 143
Performance Analysis of 2x2 MIMO for OFDM-DSSS
Based Wireless System
Mr. Bhavin Gamit*
Prof. Shweta N. Shah
Electronics, SVNIT Electronics, SVNIT
Abstract— In today’s 3G world moving to 4G requires high data rate support in applications like multimedia services,
internet access and video streaming services. Such applications are always in need of very high speed data rate
support which increases the requirement of efficient usage of spectrum and high capacity systems. Thus the major
challenges to be taken care of in designing the next generation wireless communications system should provide or
accommodate capacity, the spectral efficiency, improved link reliability and multimedia services. So we can establish a
distributed system in terms of multi-carrier, multi-antenna and coded pulse. It gives rise to hybrid technology based on
DSSS, OFDM, and MIMO system which can be the ultimate solution for wireless cellular communication systems. In
this paper we analysis the performance of MIMO-OFDM-DSSS system. This paper also includes comparison of
performances of MIMO-OFDM-DSSS system with ZF and MMSE equalizer on the basis of BER using different
modulation techniques in a scattering environment.
Keywords— DSSS, OFDM, MIMO, ZF, MMSE, BER, BPSK, QPSK, 16-QAM, ISI, CCI.
I. INTRODUCTION
Wireless communication systems are pulling in popularity, specially with respect to mobile telephones and wireless
data devices because of their easiness of function and mobility. Initially, wireless systems were mainly designed and
developed to support voice. As multimedia applications gaining popularity, the need for high rate data services is
growing. Hence they require higher data rate with quality of service (QoS). The major challenges in future wireless
communication system will be increased capacity, spectral efficiency, link reliability and multimedia services [1][2].
Electromagnetic waves reflect, diffract, and scatter so they alleviating the demand for direct line of sight between
sender and receiver and also creating the complicated multipath problem. Multipath is the virtually infinite number of
ways that a signal can take from sender to receiver, reflecting off smooth objects, scattering off rough objects, and
diffracting around sharp corners. Multipath problem can be resolved by the DSSS. Spread spectrum originated as a
means of secure communication in the military provided spreading of signal in the frequency domain to give a very low
peak power [3]. To observers, a spread spectrum signal looks similar to white noise, and hence has a Low Probability of
Intercept (LPI). Recently, IEEE 802.11n Task Group (TGn) was organized with the goal of increasing the application
throughput to at least 100Mbps by making modifications in the PHY and MAC layer. The major variety in the PHY layer
is the uses of multiple transmit and receive antennas along with the Orthogonal Frequency Division Multiplexing
(OFDM) system to support several parallel streams which is likewise known as multicarrier based technique. It can
mitigating ISI by addition of cyclic prefix as one of the option in OFDM. It can also improve capacity in the wireless
system with spectral efficiency (bps/Hz) and reduces receiver complexity in wireless broadband systems. It is rather
tolerant of the ever present interference in the bands where it is used because it transmits hundreds of symbols
simultaneously at a low rate per symbol [4]. There are many techniques for sustaining high data rate transmission, such
as the Multiple Input Multiple Output (MIMO) system. Digital communication using MIMO processing comes forth as a
breakthrough for revolutionary wireless systems. It solves two of the hardest problems facing any wireless technology
today: speed and range. MIMO system promises to increase data rates, reliability and performance with acceptable BER
without increasing the total transmission power and also without consuming extra radio frequency [5]. But this MIMO
cannot achieve zero ISI and hence cannot be used alone. The MIMO signalling can easily be overlaid on an OFDM based
system. The MIMO signalling treats each subcarrier in OFDM as an independent narrowband flat channel. It can be
viewed as N parallel MIMO systems operating with flat fading channel coefficients. The use of MIMO technology in
combination with OFDM seems to be an attractive solution for future fastest broadband wireless systems. MIMO-OFDM
can support a higher data rate as well [2]. But MIMO suffer from co-channel interference problem [6]. It is the
interference between users in different cells and using same frequency (known as frequency reuse), results in CCI
[7].Solution to that is to apply DSSS techniques based on spread spectrum technology. The main advantage of applying
DSSS is that it reduces power per signal, That may mitigate CCI problem in MIMO-OFDM based system. By
considering problem solving capabilities of different technologies, Here, Hybrid system based on MIMO, OFDM and
DSSS techniques is analysed.
We present the performance analysis and simulation of a MIMO-OFDM-DSSS with ZF and MMSE equalizers using
different modulation techniques in Rayleigh channel to reduce CCI and to improve performance. We present the MIMO-
OFDM-DSSS based hybrid system model in section II. A short description of ZF and MMSE equalization techniques is
given in section III. The simulation results and conclusion are presented and discussed in section IV and V respectively.

2. International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163
Volume 1 Issue 5 (June 2014) http://ijirae.com
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© 2014, IJIRAE- All Rights Reserved Page -144
II. SYSTEM MODEL
Fig 1 shows the simple model of MIMO-OFDM-DSSS system. The input data are initially sent to scrambler block.
Scrambler block scrambles the input data using linear feedback shift register whose configuration specifies using the
scramble polynomial parameter. The signal is then sent to the convolution encoder having mother code rate of ½. The
signal is then forwarded to an interleaver. The idea of interleaving is to disperse a block of data in frequency so that the
entire block does not experience deep fade in the channel. This prevents burst errors at the receiver. Otherwise the
convolution decoder will not perform very well in presence of burst errors. The interleaved data are grouped together to
form symbols. The symbols are then modulated using any of the modulation schemes like, BPSK, QPSK, 16QAM. The
Tx diversity block provides spatial diversity to modulated signal. In the figure we are considering 2x2 MIMO systems.
The Tx diversity encoder splits the data into 2 orthogonal streams which are processed through individual OFDM blocks.
The first stream will fed to the top IFFT modulator and second stream will be fed to the lower IFFT modulator. Here
individual OFDM signals are generated by inserting pilot carriers and CP for channel estimation. Hence the information
is transmitted in packets through the multiple transmitters. The Rx does exact reverse process of transmitter to retrieve
the transmitted information back. Due to presence of MIMO technique, equalization is required at receiver side which is
explained in next section.
Fig. 1 system model of MIMO-OFDM-DSSS system
III. EQUALIZER
Equalization is to palliate the effects of ISI to decrease the probability of error that occurs without suppression of ISI,
but this reduction of ISI effects has to be balanced with prevention of noise power enhancement. Here we present the
most common detection techniques ZF and MMSE.
A. Zero Forcing Equalizer
The Zero-Forcing Equalizer applies the inverse of the channel to the received signal, to re-establish the signal before
the channel. The name Zero forcing corresponds to taking down the Inter Symbol Interference (ISI) to zero in a noise
free case [10]. This will be useful when ISI is more predominant when comparing to the noise. For a channel with
frequency response F (f) the zero forcing equalizer C (f) is constructed by C (f) = 1 / F (f). So the combination of channel
and equalizer gives a flat frequency response and linear phase F (f) C (f) = 1.
Take a 2x2 MIMO channel, the received signal on the first receive antenna is:
(1)
= + (2)
The received signal on the Second receive antenna is:
(3)
= + (4)

3. International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163
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Where & are the received symbol on the foremost and second antenna respectively, and is the channel from
transmit antenna to receive antenna. & are the transmitted symbols and & are the noise on and
receive antennas.
The equation can be represented in matrix notation as follows:
(5)
Equivalently,
= + (6)
To solve for x, we need to see a matrix W which satisfies WH = I. The Zero Forcing (ZF) detector for meeting this
constraint is made by:
= (7)
Where W is the Equalization Matrix and H is Channel Matrix [11].
B. MMSE EQUALIZER
A Minimum Mean Square Error (MMSE) estimator describes the approach which minimizes the Mean Square Error
(MSE), which is a coarse measure of estimator quality. The primary feature of MMSE equalizer is that it does not usually
eliminate ISI completely, but, minimizes the full force of the noise and ISI components in the output. Let x and y be an
unknown random variables [12]. An estimator x^(y) is any function of the measurement y, and its mean square error is
given by:
= {( − )} (8)
The mathematical expression for the MMSE equalizer for the received signal on the foremost and second antenna is
same as in the case of ZF equalizer. The only deviation in this instance is the noise added to the equalization matrix.
= (9)
IV.SIMULATION RESULTS
Fig 2 (a) and (b) shows the BER vs SNR curves for 2x2 MIMO-OFDM-DSSS system with BPSK modulation using
ZF and MMSE equalization techniques. We can show in fig 2(a) that to achieve BER we need 25 SNR using ZF.
Fig 2(b) shows that to achieve BER we need 25 SNR using MMSE. In the fig 2 (a) and (b) for BPSK modulation
technique the minimum values of bit error rate are: for ZF equalizer min. BER is 0.029375. Similarly for MMSE
equalizer min BER is 0.02715 at SNR 22.
(a) (b)
Fig. 2 BER vs SNR with BPSK for (a) ZF and (b) MMSE
Fig 3(a) and (b) shows the BER vs SNR curves for 2x2 MIMO-OFDM-DSSS system with QPSK modulation.

4. International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163
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(a) (b)
Fig. 3 BER vs SNR with QPSK for (a) ZF and (b) MMSE
We can show in figure 3(a) that to achieve BER we need 25 SNR using ZF. We can also show that to achieve
BER we need 25 SNR using MMSE in fig 3(b). In the fig 3 (a) and (b) for QPSK modulation technique the
minimum values of bit error rate are: for ZF equalizer min. BER is 0.025825. Similarly for MMSE equalizer min BER is
0.026311 at SNR 22.
(a) (b)
Fig. 4 BER vs SNR with 16-QAM for (a) ZF and (b) MMSE

5. International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163
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Fig 4(a) and (b) shows the BER vs SNR curves for 2x2 MIMO-OFDM-DSSS system with 16-QAM modulation using
ZF nad MMSE equalization techniques. We can show in fig 4(a) that to achieve BER we need 24 SNR using ZF.
We can show that to achieve BER we need 25 SNR. In the fig 4 (a) and (b) for 16-QAM modulation technique the
minimum values of bit error rate are: for ZF equalizer min. BER is 0.041875. Similarly for MMSE equalizer min BER is
0.00865 at SNR 22.
V. CONCLUSIONS
In this paper the concept of MIMO-OFDM-DSSS based hybrid system using different equalizer over a multipath
Rayleigh fading channel is presented. The idea of hybrid technology is implement and analyse by plotting the SNR v/s
BER performance for MIMO-OFDM-DSSS system. The MIMO-OFDM-DSSS system is implemented using different
equalization techniques like ZF and MMSE. The MMSE gives best performance for all three modulation techniques
BPSK, QPSK and 16-QAM. ZF is a gives fairer performance compare to MMSE for lower order modulation techniques.
MMSE can give best performance for high order modulation techniques. 16-QAM gives best result among all three
modulation techniques.
REFERENCES
[1] G. J. Foschini, M. J. Gans. “On limits of wireless communications in a fading environment when using multiple
antennas”. Wireless Pers. Communication, vol. 6, pp.311-335, March 1998.
[2] Jiang Xuehua , Chen Peijiang, ”Study and Implementation of MIMO OFDM In matlab Simulation”, in
International Conference on Information Technology and Computer Science, 26th July 2009.
[3] Yao Liu, Peng Ning, Huaiyu Dai, An Liu “Randomized Differential DSSS: Jamming-Resistant Wireless
Broadcast Communication” at IEEE INFOCOM, ISBN 978-1-4244-5836-3, pp.1-9, 2010.
[4] Ramjee Prasad “OFDM for Wireless Communications Systems, Artech House Universal Personal
Communications series”, proceedings of the IEEE, 2004.
[5] Q. H. Spencer, C. B. Peel and A.lee, “An Introduction to the Multi-User MIMO Downlink,” IEEE Commun.
Mag., vol. 42, no. 10, pp. 60-67, Oct. 2004.
[6] Muhammad Imadur Rahman, Elisabeth de Carvalho, Ramjee Prasad “Mitigation of MIMO Co-Channel
Interference using Robust Interference Cancellation Receiver”, IEEE 66th
Vehicular Technology Conference,
pp.531-535, 2007.
[7] Anas M. Salhab And Ghandi F. Manasra “Performance Analysis of A Beyond 3G Mobile Communication
System in the Presence of Co-channel Interference”, Wseas Transactions On Electronics Manuscript, Issue 6,
Volume 4, June 2007.
[8] Nirmalendu Bikas Sinha, R.Bera, S.Manna, Shashank Gupta, “ Hybrid Technology for Next Generation Broad
band Mobile Radio Communications”, IEEE conference on wireless and optical communication network,, pp. 1-
5, April 2009.
[9] N. Tamilarasan L. Nithyanandan “Performance of the MIMO MC-CDMA System with Beamforming”, 4th
ICCCNT 2013 in Tiruchengode, India IEEE – 31661, pp.1-5 July 2013.
[10]Abdessalem Trimeche, Nesrine Boukid, Anis Sakly, Abdellatif Mtibaa “Performance Analysis of ZF and
MMSE Equalizers for MIMO Systems”, IEEE International Conference on Design & Technology of Integrated
Systems in Nanoscale Era, pp.1-6, 2012.
[11]Bhasker Gupta and Davinder S.Saini “BER Performance Improvement in MIMO Systems Using Various
Equalization Techniques”, 2nd IEEE International Conference on Parallel, Distributed and Grid Computing
Waknaghat, pp.90-194, Dec 2012.
[12]Manish Kumar and Jasmeet Kaur “Performance analysis of BPSK system with ZF & MMSE equalization”,
International Journal of Latest Trends in Engineering and Technology (IJLTET), Vol. 1 Issue 3 September 2012.